Every boardroom conversation about AI eventually hits a wall of jargon. Vendors throw around acronyms. Consultants speak in abstractions. And somewhere in the room, a CEO nods along while quietly wondering what RAG actually stands for and whether it matters for their operation in Medellín or Monterrey.
It does matter — because understanding the vocabulary is the first step to asking the right questions, evaluating the right vendors, and making decisions that don't age badly. This glossary cuts through the noise. No definitions written for engineers. Just the terms you'll encounter most often, explained the way a senior colleague would explain them over lunch.
An AI agent is a system that can take a sequence of actions to complete a goal — not just answer a question. It can browse the web, query a database, send an email, or call another software tool, chaining steps together with minimal human input. "Agentic" workflows are those where the AI is making decisions along the way, not just generating text. This is the architecture behind the most powerful enterprise automations being built right now.
Why it matters to you: An agent can handle an end-to-end process — say, pulling a supplier invoice, cross-referencing it with inventory records, and flagging discrepancies — without a human touching it at each step.
The context window is how much text (or data) a model can "see" at once during a conversation or task. Think of it as the model's working memory. A small context window means the model forgets earlier parts of a long document. A large one lets it process entire contracts, reports, or email threads in one pass.
Why it matters to you: If you're using AI to analyze lengthy documents or long customer threads, the context window of the model you choose directly affects quality and reliability.
Fine-tuning is the process of taking a general-purpose AI model and training it further on your specific data so it behaves more accurately in your domain. A fine-tuned model for a Colombian insurance company will understand local policy language, industry jargon, and common customer queries far better than a generic model will.
Why it matters to you: Fine-tuning costs time and money. For most companies, RAG (see below) is the faster and cheaper starting point. Fine-tuning makes sense once you have a clear, narrow use case with enough proprietary data to justify it.
A foundation model is a large AI model trained on massive, broad datasets — the base layer that other products and tools are built on top of. GPT-5, Claude, Gemini, and Llama are all foundation models. Most businesses never interact with foundation models directly; they use them through APIs or through products built on top of them.
Why it matters to you: Knowing which foundation model powers a vendor's product helps you assess its capabilities, limitations, and data privacy implications.
Grounding refers to connecting an AI model to real, verified data sources — so it generates outputs based on facts rather than pattern-matching from training data. A grounded model pulls from your internal knowledge base, live databases, or the web before responding. It's closely related to RAG.
Why it matters to you: Grounding is the practical antidote to hallucination (see below). Any AI deployment that needs to produce accurate, business-critical information should be grounded.
Guardrails are rules and controls put in place to constrain what an AI system can say or do. They prevent the model from going off-script — producing offensive content, sharing confidential data, or taking unauthorized actions. In enterprise deployments, guardrails are a governance layer, not an optional feature.
Why it matters to you: Before deploying any customer-facing or internal AI, ask your vendor or team: what guardrails are in place, and who can modify them?
When an AI model confidently states something that is factually wrong. Not a bug in the traditional sense — it's a structural behavior that emerges from how these models generate text. The model produces plausible-sounding sequences, not verified facts. KPMG learned this the hard way when it had to retract an AI-generated report earlier this year.
Why it matters to you: Hallucination is the primary reason AI outputs need human review in any context where accuracy is non-negotiable: legal documents, financial reports, client communications.
Inference is what happens when you use an AI model — when it takes your input and generates a response. Training is building the model; inference is running it. Inference costs money (compute), and at scale, inference efficiency becomes a real operational concern.
Why it matters to you: When evaluating AI tools, inference costs (usually charged per token) are part of the total cost of ownership, not just the subscription fee.
The class of AI model that powers most of the tools you're already encountering: ChatGPT, Claude, Gemini, Copilot. These models are trained on enormous text datasets and are capable of understanding and generating human language at high quality. "Large" refers to the number of parameters — the internal variables that define how the model processes information.
Why it matters to you: LLMs are the engine. Everything else — agents, RAG, fine-tuning — is the chassis built around them.
An open standard that defines how AI models communicate with external tools, data sources, and services. Think of it as a universal connector: if a tool supports MCP, any MCP-compatible agent can plug into it without custom integration work. It's rapidly becoming the standard for enterprise AI connectivity.
Why it matters to you: When evaluating AI vendors, MCP compatibility signals that their product can integrate into a broader ecosystem without expensive bespoke development.
A multimodal AI model can process and generate more than one type of data — text, images, audio, video, documents. The latest generation of models (GPT-5, Gemini 2.5) are multimodal by default. This opens up use cases that were impossible just two years ago: analyzing a photo of a damaged product, reading a scanned invoice, or interpreting a chart.
Why it matters to you: If your operation involves any non-text data — which it almost certainly does — multimodal models unlock document processing, visual QA, and more without separate specialized tools.
A prompt is the instruction you give an AI model. Prompt engineering is the discipline of crafting those instructions precisely to get reliable, high-quality outputs. Good prompts include context, format requirements, examples, and explicit constraints. Bad prompts produce inconsistent results — which leads to the mistaken conclusion that "AI doesn't work."
Why it matters to you: Prompt quality is the single fastest lever you can pull to improve the output of any AI tool your team is already using.
RAG is a technique where an AI model is connected to an external knowledge base. Before generating a response, the model retrieves relevant documents or data from that knowledge base and uses them as context. The result: answers grounded in your actual information, not generic training data.
Why it matters to you: RAG is currently the most practical architecture for enterprise AI that needs to know your specific policies, products, clients, or operations — without retraining the model from scratch.
Tokens are the units AI models use to process text — roughly equivalent to a word or a word fragment. Models are priced by token consumption (input + output). A 1,000-word document is approximately 1,300 tokens. Understanding tokens helps you estimate costs and understand why context windows have limits.
Why it matters to you: Every AI API call consumes tokens. At volume, token efficiency is a cost management issue, not just a technical detail.
Literacy precedes leverage. Business leaders who understand these terms don't just avoid being misled by vendors — they ask sharper questions, set better expectations with their teams, and make faster decisions when it counts.
The companies in LATAM that are moving fastest on AI right now aren't the ones with the biggest budgets. They're the ones where leadership has enough fluency to separate signal from noise.
If your team is working through which of these capabilities apply to your operation — and in what order — that's exactly the conversation Xenturia is built for.
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